of remote sensing application? What is the accuracy of the data product, and retrieval product? The following section offers examples of validation techniques for remotely sensed climate data.
Techniques for validating remotely sensed data vary for different geophysical parameters (e g., winds, aerosols, precipitation, clouds). The most common technique is comparing ground measurements to remote sensing observations or modeled results. In many situations, a mismatch exists between the sensor’s field of view and the scale at which in situ measurements are collected. Ground-based measurements cover small spatial scales while satellite retrievals cover an area of many kilometers. However, in the process of working through a validation, the real structure of the data can be revealed. This was nicely described at the workshop by Tom Bell from the National Aeronautics and Space Administration (NASA), who presented challenges in remote sensing of precipitation. The use of in situ measurements for model calibration and validation requires a robust method to spatially aggregate ground measurements to the scale at which the remotely sensed data are acquired (Box 2-1).
As previously mentioned, defining the uncertainty of model parameters is a continuing challenge, but there are multiple methodologies in validation studies that can be combined in an optimal way. Examples described at the workshop were studies to understand fluxes in atmospheric carbon dioxide (CO2). Some studies will employ measurements of CO2 concentration to infer sources and sinks, while other studies build biosphere models in an attempt to predict the fluxes. These can be combined, using the biospheric models as a first guess followed by a Bayesian framework to integrate the modeled outputs with atmospheric data to get a best estimate of carbon sources and sinks. In this approach, there is an opportunity to account for the uncertainty in the individual parameters as well as the modeling framework that is used to predict the processes of interest.
Inherent in many different techniques that are used in processing remotely sensed data is the issue of biases. A workshop participant described that bias in validation studies of some geophysical parameters occurs because of the uneven global distribution of surface cloud observations. The oceans tend to be cloudier on average than most of the land, but there are fewer surface observations over the oceans. For example, if a threshold is set for the number of surface observations present in a 2.5 degree grid box before accepting a data point, the global mean that is calculated will depend on that threshold. A threshold will therefore force parts of the earth (i.e., the southern oceans), which are known to be very cloudy, to be omitted from the averaged data. Furthermore, the samples do not stay constant; measurements in a grid box can change from month to month, which introduces a source of variability that is generated from